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Lin Luo,1– 3 Wen Wen,4 Chun-yi Wang,4 Mengyun Zhou,5 Jie Ni,6 Jingjie Jiang,4 Juan Chen,4 Ming-wei Wang,4 Zhanhui Feng,6 Yong-Ran Cheng7 1Hangzhou Ruolin Hospital Management Co. Ltd, Hangzhou, 310007, People’s Republic of China; 2Hangzhou Kaihong Technology Co., Ltd, Hangzhou, 310059, People’s Republic of China; 3Jiangxi Key Laboratory of Natural Products and Functional Food, College of Food Science and Engineering, Jiangxi Agricultural University, Nanchang, 330045, People’s Republic of China; 4Hangzhou Institute of Cardiovascular Diseases, Hangzhou Medical Key Discipline, Affiliated Hospital of Hangzhou Normal University, Hangzhou, 310015, People’s Republic of China; 5Department of Molecular & Cellular Physiology, Shinshu University School of Medicine, Nagano, 3900803, Japan; 6Internal Medicine-Neurology, Affiliated Hospital of Guizhou Medical University, Guiyang, People’s Republic of China; 7School of Public Health, Hangzhou Medical College, Hangzhou, 311300, People’s Republic of ChinaCorrespondence: Yong-Ran Cheng; Zhanhui Feng Email chengyr@zjams.com.cn; h9450203@126.comBackground and Aim: Relevant studies show that population migration has a great impact on the early spread of infectious diseases. Therefore, it is important to explore whether there is an explicit relationship between population migration and the number of confirmed cases for the control of the COVID-19 epidemic. This paper mainly explores the impact of population migration on early COVID-19 transmission, and establishes a predictive nonlinear mathematical model to predict the number of early cases.Methods: Data of confirmed cases were sourced from the official website of the Municipal Health Committee, and the proportions of migration from Wuhan to other cities were sourced from the Baidu data platform. The data of confirmed cases and the migration proportions of 14 cities in Hubei Province were collected, the COVID-19 cases study period was determined as 10 days based on the third quartile of the interval of the incubation period, and a non-linear mathematical model was constructed to clarify the relationship between the migration proportion and the number of confirmed COVID-19 cases. Finally, eight typical regions were selected to verify the accuracy of the model.Results: The daily population migration rates and the growth curves of the number of confirmed cases in the 14 cities were basically consistent, and Pearson’s correlation coefficient was 0.91. The specific mathematical expression of 14 regions is . In each of the fourteen cities, The nonlinear exponential model structure is as follows:. It was found that the R2 values of the fitted mathematical model were greater than 0.8 in all studied regions, excluding Suizhou (p < 0.05). The established mathematical model was used to fit eight regions in China, and the correlations between the predicted and actual numbers of confirmed cases were greater than 0.9, excluding that of Hebei Province (0.82).Conclusion: The study found that population migration has a positive and significant impact on the spread of COVID-19. Modeling COVID-19 risk may be a useful strategy for directing public health surveillance and interventions. Restricting the migration of the population is of great significance to the joint prevention and control of the pandemic worldwide.Keywords: COVID-19, SARS-CoV-2, spreads, travel, non-linear exponential

At the end of 2019, the COVID-19 emerged in Wuhan, China. It has since put global public health institutions on high alert. People in China reduced their traveling, and production has stopped nationwide during the height of the epidemic. This study explores the effects of these COVID-19-derived changes on air quality in China. Air quality data of 367 cities around China were analyzed. The daily air quality index and air pollutant concentrations (CO, O3, NO2, SO2, PM10, and PM2.5) were collected and compared the epidemic period (23.1.2020-23.3.2020) with the preceding two months (22.11.2019-22.1.2020) and the parallel period the year before (23.1.2019-23.3.2019).To compare, we calculated the daily average number of cities with pollution, and the trend in air quality index change. The air quality in the 50 cities with the highest number of confirmed COVID-19 cases and Wuhan was also analyzed. During the period between 23.1.2020 and 23.3.2020, the number of cities with excellent air quality was significantly higher than that in the other two periods. The concentrations of PM2.5, PM10, NO2, SO2, CO, and O3 decreased significantly during this period. The most significant decreases were in PM10 and NO2. The number of cities with good air quality in the later period was significantly higher than a year before. The air quality has improved significantly during the COVID-19 outbreak. The reason for this change might be changes in human activities such as reduced transportation and production stoppage.

Ming-Wei Wang,1,&ast; Lixia Sun,2,&ast; Wen Wen,1,&ast; Jie Wang,3 Chun-yi Wang,1 Jie Ni,1 Jing-jie Jiang,1 Zhan-Hui Feng,4 Yong-Ran Cheng5 1Metabolic Disease Center, Affiliated Hospital of Hangzhou Normal University, Hangzhou, People’s Republic of China; 2Zhejiang University of Water Resources and Electric Power, Hangzhou, People’s Republic of China; 3Hangzhou Zhenqi Technology Co., Ltd, Hangzhou, People’s Republic of China; 4Neurological Department, Affiliated Hospital of Guizhou Medical University, Guiyang, People’s Republic of China; 5School of Public Health, Hangzhou Medical College, Hangzhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Zhan-Hui Feng; Yong-Ran Cheng, Email h9450203@126.com; chengyr@zjams.com.cnBackground: Many studies have shown that the pollution of fine particles in the air is related to the incidence of chronic diseases. However, research on air pollution and metabolism-associated fatty liver disease (MAFLD) is limited.Objective: The purpose of this study was to explore the relationship between short-term ambient air pollution and daily outpatient visits for metabolic-related fatty liver.Methods: We used a quasi-Poisson regression generalized additive model to stratify analyses by season, age, and gender.Results: From January 1, 2017, to August 31, 2019, 10,562 confirmed MAFLD outpatient visits were recorded. A 10 μg/m3 increase of fine particular matter (PM10and PM2.5) and NO2 concentrations corresponding with percent change were 0.82 (95% confidence interval [CI], 0.49– 1.15), 0.57 (95% CI, 0.18– 0.98), and 0.86 (95% CI, 0.59– 1.13) elevation in MAFLD outpatient visits. In terms of season, the impact estimates of NO2 and PM2.5% change were 3.55 (95% CI, 1.23– 5.87) and 1.12 (95% CI, 0.78– 1.46) in the hot season and transition season, respectively. Compared with the warm season, the impact estimates of PM10were more significant in the cool season: 2.88 (95% CI, 0.66– 5.10). NO2 has the greatest effect in the transition season, whereas PM10 has the greatest highest effect in the cool and hot seasons. Compared with other pollutants, PM2.5 has the greatest impact in the age stratification, which percent change are 2.69 (95% CI, 0.77– 5.61) and 2.88 (95% CI, 0.37– 6.40) respectively. The impact values of PM2.5 in male and female percent change were 3.60 (95% CI, 0.63– 6.57) and 1.65 (95% CI, 1.05– 2.25), respectively.Conclusion: This study shows that the air pollutants are related to the number of outpatient visits for MAFLD. The effects of different air pollutants on MAFLD outpatient visits were different by season, ages, and gender.Keywords: air pollutants, metabolism-associated fatty liver disease, generalized additive model

Juan Chen,1,* Yong-Ran Cheng,2,* Wen Wen,1,* Chunyi Wang,1,* Jie Ni,1 Jingjie Jiang,1 Xinyan Fu,1 Mengyun Zhou,3 Lan Ye,4 Zhong-Jun Ge,5 Han-Xia Tan,6 Mingwei Wang,1 Zhan-Hui Feng,7 Xingwei Zhang1 1Department of Cardiology, Hangzhou Institute of Cardiovascular Diseases, Affiliated Hospital of Hangzhou Normal University, Hangzhou, People’s Republic of China; 2School of Public Health, Hangzhou Medical College, Hangzhou, People’s Republic of China; 3Department of Molecular and Cellular Physiology, Shinshu University School of Medicine, Nagano, Japan; 4Basic Medical College, Guizhou Medical University, Guizhou, People’s Republic of China; 5Department of Cardiology, The Fourth People’s Hospital of Lin’an, Hangzhou, People’s Republic of China; 6Department of Oncology, The Third People’s Hospital of Hangzhou, Hangzhou, People’s Republic of China; 7Department of Neurology, Affiliated Hospital of Guizhou Medical University, Guiyang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhan-Hui FengAffiliated Hospital of Guizhou Medical University, No. 28, Guiyi Street, Guiyang, People’s Republic of ChinaEmail h9450203@126.comXingwei ZhangHangzhou Institute of Cardiovascular Diseases, Affiliated Hospital of Hangzhou Normal University, 126 Wenzhou Road, Gongshu District, Hangzhou, People’s Republic of ChinaEmail hsdzxw@126.comBackground: Many people have experienced novel coronavirus pneumonia since the beginning of the COVID-19 pandemic in Wuhan, China. The Chinese government has encouraged people to wear face masks in public places; however, due to the large population, there may be a series of problems related to this recommendation, including shortages of masks and lack of an optimal disposal method for used masks.Objective: The purpose of this study is to understand the current status of mask shortages and used masks in China.Methods: A questionnaire survey was designed to assess the current status of mask shortages and used masks. The differences among groups were analyzed with chi-square tests.Results: The constituent ratio of those who reuse masks was 61%. Obtaining masks from the drugstore was reported to be very difficult due to high demand and short supply, and approximately 1/3 of the respondents purchased expensive masks. Most people know how to properly handle used masks, and only 7% of them casually discard masks. However, 50% of respondents have seen others throw away used masks at will. A further subgroup analysis showed that respondents in Central China tended to use masks repeatedly, as did medical personnel. Females, people living in the central region, and medical personnel may find it more difficult to purchase masks in drugstores. Non-medical personnel may be more likely to buy expensive masks. Females, people living in the western region, and medical personnel may be more likely to know how to properly handle used masks and not to discard used masks at will. Medical personnel may be more likely to observe others discarding used masks at will.Conclusion: In response to COVID-19, the public should be encouraged to use face masks and are advised not to reuse or throw away masks at will due to safety concerns.Keywords: COVID-19, mask, epidemic outbreak

Aims We aimed to evaluate the impact of the COVID-19 epidemic on emergency and cardiovascular disease–related calls in Hangzhou, China. Methods We conducted a single-center retrospective study, collecting data on emergency calls to the Hangzhou Emergency Center (HEC) during the COVID-19 epidemic (January 20, 2020, to March 15, 2020). Data were compared with the same period in 2019. Results Compared to 2019, the number of emergency calls has dropped by 21.63%, ambulance calls by 29.02%, rescue calls by 22.57%, and cardiovascular disease-related emergency calls by 32.86%. The numbers of emergency, ambulance, and rescue calls in 2020 were significantly lower than in 2019. Conclusions During the COVID-19 epidemic in Hangzhou, the numbers of emergency and cardiovascular disease–related calls have decreased significantly. These results point to a severe social problem that requires the attention of the medical community and the government.

Objectives: The aim of this study was to determine the current knowledge levels on cardiopulmonary resuscitation and automated external defibrillators of Chinese citizens. Methods: A face-to-face questionnaire survey on the knowledge of and attitudes toward the use of CPR and AEDs during an OHCA event was conducted in three high-traffic train stations in Hangzhou and Taizhou, China. The survey included questions on previous experience with CPR, recognition of cardiac arrest, CPR-related concerns, and AED knowledge. Results: A total of 6268 participants were surveyed about their knowledge of and attitudes toward the use of CPR and AEDs. A total of 5981 valid questionnaires, including 3008 from female respondents (50.2%) and 2973 from male respondents (49.8%), were retrieved. The participants were geographically distributed as follows: 18.5% from western China, 19.5% from central China, and 77.0% from eastern China. The educational level, regular residence, and sex of the respondents, as well as legislative policies, were relevant to the knowledge of and attitudes toward the use of CPR and AEDs for OHCA events. The results showed that men were more active than women in terms of CPR training and implementation, as well as had more knowledge and used AEDs more often. More people in eastern and central China had witnessed CPR performance, would pay for CPR training, and would be willing to perform CPR if there was a related legislative policy. Moreover, educational level was crucial to the knowledge of and attitudes toward the use of CPR and AEDs during OHCA events. Conclusions: Chinese citizens lack CPR and AED knowledge and training compared with people in developed countries. We recommend that the state should increase public awareness of CPR and AEDs in China. Mass education, policy implementation, specialized training, and legislative action should be carried out.

The goal of this study is to explore the clinical value of routine tests in multiple myeloma (MM).A total of 179 MM patients, newly diagnosed in our hospital from January 2010 to December 2018 (case group), as well as 352 cases of healthy individuals (control group) were evaluated. Albumin (Alb), globulin (Glb), albumin/globulin (A/G), creatinine (Cr), calcium (Ca), hemoglobin (Hb), lactate dehydrogenase (LDH), platelet count (Plt), and platelet distribution width (PDW) were compared between the analyzed groups. Respective tests were screened by forward selection. Thereafter, screened out indicators were identified through logistic regression analysis. Risk prediction nomogram, area under curve (AUC), calibration, decision curve analysis (DCA), and clinical impact curve (CIC) were further performed. At the same time, routine test indicators of MM patients for stage and subtype diagnosis, were compared. A correlation analysis between these test indicators and respective disease stages was performed. High stage group and low stage groups were subsequently compared to define the predictive value of single and combined indicators of disease severity.Except for Ca, the difference between the case and control groups for all other blood indicators was statistically significant (p0.05). Moreover, the difference in positive rate(s) was statistically significant (p0.05). The receiver operating characteristic (ROC) curve of Alb, Hb, and PDW harbored robust discrimination (AUC = 0.960) and appropriate calibration. The DCA and CIC showed that the resulting nomogram had a superior net benefit in predicting MM. Among all indicators, only LDH was statistically reduced in MM patients at ISS stages I, II, and III (p0.05). Interestingly, the ISS stage of respective MM patients was positively correlated with Cr (τ = 0.392), while it was negatively correlated with Hb (τ = -0.364). Alb, Glo, A/G, and Hb were significantly distinct between heavy chain (IgG, IgA) and LC, while few significant differences were found between the ISS stages. Lastly, the AUC (0.828) for Cr was greater than that for all other single and combined indicators.The effective application of major indicators measured in routine blood tests can provide important clues for the diagnosis and prognosis of MM.

Aim: Metabolic syndrome (MS) screening is essential for the early detection of the occupational population. This study aimed to screen out biomarkers related to MS and establish a risk assessment and prediction model for the routine physical examination of an occupational population.Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. The areas under the receiving operating characteristic curves were used to evaluate the selection accuracy of biomarkers in identifying MS subjects with risk. The screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. A nomogram risk prediction model was established based on the selected biomarkers, and the consistency index (C-index) and calibration curve were derived.Results: A total of 2,844 occupational workers were included, and 10 biomarkers related to MS were screened. The number of non-MS cases was 2,189 and that of MS was 655. The area under the curve (AUC) value for non-Lasso and Lasso logistic regression was 0.652 and 0.907, respectively. The established risk assessment model revealed that the main risk biomarkers were absolute basophil count (OR: 3.38, CI:1.05–6.85), platelet packed volume (OR: 2.63, CI:2.31–3.79), leukocyte count (OR: 2.01, CI:1.79–2.19), red blood cell count (OR: 1.99, CI:1.80–2.71), and alanine aminotransferase level (OR: 1.53, CI:1.12–1.98). Furthermore, favorable results with C-indexes (0.840) and calibration curves closer to ideal curves indicated the accurate predictive ability of this nomogram.Conclusions: The risk assessment model based on the Lasso logistic regression algorithm helped identify MS with high accuracy in physically examining an occupational population.

BACKGROUND: Since the end of 2019, the coronavirus disease (COVID-19) outbreak rapidly became a pandemic. The psychological state of people during the COVID-19 pandemic has gained interest. Our aim was to study the prevalence of anxiety, depression, and stress in college students during the COVID-19 pandemic. Methods: A systematic search of Medline, Embase, Web of Science, and the Cochrane Library was conducted up to September 20, 2020. Reviewers independently assessed full-text articles according to predefined criteria. Stata14/SE was used to calculate the prevalence and 95% confidence intervals (CIs) of anxiety, depression, and stress among college students from different countries. A random effects model was adopted. The Egger test was used to determine publication bias. Results: A total of 280 references were retrieved, and 28 papers met our inclusion criteria, for a total of 436,799 college students. Thirteen studies involved non-Chinese college students, and 15 studies involved Chinese college students. The prevalence of anxiety, depression, and stress was 29% (95% CI, 19-25%), 37% (95% CI, 32-42%), and 23% (95% CI, 8-39%), respectively. Conclusion: The COVID-19 pandemic has had a negative psychological effect on college students, and the prevalence of anxiety, depression, and stress among Chinese college students is lower than among non-Chinese college students.

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Background Many studies have shown that the fine particulate matter in air is related to the incidence rate of chronic diseases. However, research on air pollution and metabolism-associated fatty liver disease (MAFLD) is limited. The purpose of this study was to explore the relationship between the incidence rate of MAFLD and air pollutants.Methods using a quasi-Poisson regression generalized additive model. Stratified analyses by season and age were also performed. Results A 10 µg/m3 increase of PM10, PM2.5, and NO2 concentrations corresponded to 0.82 (95%Cl: 0.49, 1.15), 0.57(95%Cl: 0.18,0.98), and 0.86(95%Cl: 0.59,1.13) elevation in MAFLD. In terms of season, the impact estimates of NO2 and PM2.5 were 3.55 (95% CI, 1.23-5.87) and 1.12 (95% CI, 0.78-1.46) in the hot season and transition season, respectively. Compared with warm season, the impact estimates of PM10 were more significant in the cool season: 2.88 (95% CI, 0.66-5.10). NO2 has the highest effect in the transition season, while PM10 has the highest effect in the cool and hot seasons. In the two age groups with 45 years as the dividing line, PM2.5 has the highest impact estimate: 2.69 (95% CI, 0.77-5.61) and 2.88 (95% CI, 0.37-6.40). The impact values of PM2.5 in male and Female were 3.60 (95% CI, 0.63-6.57) and 1.65 (95% CI, 1.05-2.25), respectively. The most important link is in different lag models, there is a significant correlation.Conclusion: This study shows that the air pollutants are related to the incidence rate of MAFLD. The effects of different air pollutants on MAFLD incidence rate were different in different seasons, ages, and gender. It is found that air pollution has a lag effect on MAFLD.

Objective: This study aimed to screen out biomarkers related to metabolic syndrome (MS) and establish a risk assessment and prediction model for the routine physical examination of an occupational population .Methods: The least absolute shrinkage and selection operator (Lasso) regression algorithm of machine learning was used to screen biomarkers related to MS. Then, the screened biomarkers were used to establish a logistic regression model and calculate the odds ratio (OR) of the corresponding biomarkers. Finally, the accuracy of the logistic regression model was further verified based on the Lasso regression algorithm. Results: A total of 2844 occupational workers were included, and 10 biomarkers related to MS were screened. The established risk assessment model revealed that the main risk factors were basophil absolute count (OR: 3.38), platelet packed volume (OR: 2.63), leukocyte count (OR: 2.01), red blood cell count (OR: 1.99), and alanine aminotransferase level (OR: 1.53). The area under the curve (AUC) value for non-Lasso and Lasso regression was 0.652 and 0.907, respectively. Conclusion: The risk assessment model based on the Lasso regression algorithm helped identify MS with high accuracy in physically examining an occupational population. Funding Statement: The presented study was supported by the Hangzhou Science and technology development plan projects (20140633B32;20200834M29); Youth fund of Zhejiang Academy of Medical Sciences (No.2019Y009); Medical and Technology Project of Zhejiang Province (No.2021HY127,No.2020362651,No.2021KY890); Hangzhou science and Technology Bureau fund (No.20191203B96;No.20191203B105); Clinical Research Fund of Zhejiang Medical Association(No.2020ZYC-A13); Hangzhou Health and Family Planning Technology Plan key projects（2017ZD02). Declaration of Interests: The authors declare that there is no conflict of interests regarding the publication of this paper. Ethics Approval Statement: Not applicable. My manuscript does not report on or involve the use of any animal or human data or tissue.

Objective To explore the effect of COVID-19 outbreak on the treatment time of patients with ST-segment elevation myocardial infarction (STEMI) in Hangzhou, China. Methods We retrospectively reviewed the data of STEMI patients admitted to the Hangzhou Chest Pain Center (CPC) during a COVID-19 epidemic period in 2020 (24 cases) and the same period in 2019 (29 cases). General characteristics of the patients were recorded, analyzed, and compared. Moreover, we compared the groups for the time from symptom onset to the first medical contact (SO-to-FMC), time from first medical contact to balloon expansion (FMC-to-B), time from hospital door entry to first balloon expansion (D-to-B), and catheter room activation time. The groups were also compared for postoperative cardiac color Doppler ultrasonographic left ventricular ejection fraction (LVEF),the incidence of major adverse cardiovascular and cerebrovascular events (MACCE),Kaplan-Meier survival curves during the 28 days after the operation. Results The times of SO-to-FMC, D-to-B, and catheter room activation in the 2020 group were significantly longer than those in the 2019 group (P

Background: Accurate prediction of the number of patients with conjunctivitis plays an important role in providing adequate treatment at the hospital, but such accurate predictive model currently does not exist. The current study sought to use machine learning (ML) prediction based on past patient for conjunctivitis and several air pollutants. The optimal machine learning prediction model was selected to predict conjunctivitis-related number patients.Methods: The average daily air pollutants concentrations (CO, O3, NO2, SO2, PM10, PM2.5) and weather data (highest and lowest temperature) were collected. Data were randomly divided into training dataset and test dataset, and normalized mean square error (NMSE) was calculated by 10 fold cross validation, comparing between the ability of seven ML methods to predict the number of patient due to conjunctivitis (Lasso penalized liner model, Decision tree, Boosting regression, Bagging regression, Random forest, Support vector, and Neural network). According to the accuracy of impact prediction, the important air and weather factors that affect conjunctivitis were identified.Results: A total of 84977 cases to treat conjunctivitis were obtained from the ophthalmology center of the Affiliated Hospital of Hangzhou Normal University. For all patients together, the NMSE of the different methods were as follows: Lasso penalized liner regression: 0.755, Decision tree: 0.710, Boosting regression: 0.616, Bagging regression: 0.615, Random forest: 0.392, Support vectors: 0.688, and Neural network: 0.476. Further analyses, stratified by gender and age at diagnosis, supported Random forest as being superior to others ML methods. The main factors affecting conjunctivitis were: O3, NO2, SO2 and air temperature.Conclusion: Machine learning algorithm can predict number of patients due to conjunctivitis, among which, the Random forest algorithm had the highest accuracy. Machine learning algorithm could provide accurate information for hospitals dealing with conjunctivitis caused by air factors.

Objectives At the end of 2019, the COVID-19 epidemic broke out in Wuhan, China. On January 20, 2020, Chinese expert group confirmed that the spread of the virus is characterized by human-to-human transmission. Study Design It is difficult for the public to prevent the spread of COVID-19 by only wearing masks, and the most important measureto take is to cut off the route of transmission; otherwise, there is noway to control the disease. Methods The Wuhan Municipal Governmentannounced the control of the migration of the population in Wuhan, and population migration in Hubei Province also continues to be monitored after the confirmation of human-to-human transmission.Unfortunately, some problems remain. Results At the beginning of the epidemic breakout, there were not enough hospital beds for the patients in Wuhan, and a large number of patients were required to self-solate at home. Therefore, home isolation poses significant risks. Conclusions The patient will transmit the virus to other people in the houseif a patient has been confirmed to have the virus and is under home isolation.This can lead to the infection of the patient's entire family.

Background Accurate prediction of the number of patients with conjunctivitis plays an important role in providing adequate treatment at the hospital, but such accurate predictive model currently does not exist. The current study sought to use machine learning (ML) prediction based on past patient for conjunctivitis and several air pollutants. The optimal machine learning prediction model was selected to predict conjunctivitis-related number patients. Methods The average daily air pollutants concentrations (CO, O3, NO2, SO2, PM10, PM2.5) and weather data (highest and lowest temperature) were collected. Data were randomly divided into training dataset and test dataset, and normalized mean square error (NMSE) was calculated by 10 fold cross validation, comparing between the ability of seven ML methods to predict the number of patient due to conjunctivitis (Lasso penalized liner model, Decision tree, Boosting regression, Bagging regression, Random forest, Support vector, and Neural network). According to the accuracy of impact prediction, the important air and weather factors that affect conjunctivitis were identified. Results A total of 84977 cases to treat conjunctivitis were obtained from the ophthalmology center of the Affiliated Hospital of Hangzhou Normal University. For all patients together, the NMSE of the different methods were as follows: Lasso penalized liner regression: 0.755, Decision tree: 0.710, Boosting regression: 0.616, Bagging regression: 0.615, Random forest: 0.392, Support vectors: 0.688, and Neural network: 0.476. Further analyses, stratified by gender and age at diagnosis, supported Random forest as being superior to others ML methods. The main factors affecting conjunctivitis were: O3, NO2, SO2 and air temperature. Conclusion Machine learning algorithm can predict number of patients due to conjunctivitis, among which, the Random forest algorithm had the highest accuracy. Machine learning algorithm could provide accurate information for hospitals dealing with conjunctivitis caused by air factors.

Patient delay in the recognition of and response to the symptoms of acute coronary syndrome (ACS) is a worldwide problem. A community education program about chest pain was implemented in China, and was aimed at providing better community intervention. In this study, the impact of this program on the time of symptom onset to the first medical contact (SO-to-FMC) in ACS patients was investigated, as was the incidence of major adverse cardiac and cerebrovascular events (MACCE) in these patients.A total of 10 local communities were included in this study. A 9-month intensive community education program about chest pain was conducted in these communities. The data on the demographics, mode of transportation, procedures, clinical outcomes, and discharge diagnoses of all ACS patients in these communities were collected.The study communities had a combined population of 361,609, and all community population sizes ranged from 12,823 to 66,127. The average SO-to-FMC time of the control period was 510 min, whereas, following community intervention, the average SO-to-FMC time was 256 min (P 0.001). Furthermore, comparative analyses revealed that, following discharge from the hospital, the 1.5-year MACCE-free survival rate was higher in the community intervention group than in the control group (95.0 % vs. 90.5 %, P = 0.025), and the 1.5-year mortality rate was lower in the community intervention group than in the control group (3.3 % vs. 6.3 %, P = 0.03).The Hangzhou Chest Pain Science Education Project(HCPSEP) was found to reduce the SO-to-FMC time and improve the outcome of ACS patients. This indicates that a scientific, educational program on chest pain can be effective in improving the knowledge and alertness of the local residents about chest pain. This type of program may be recognized and carried out in other regions.

As doctors, we find it disturbing to read the paper "Burnout in physicians who are exposed to workplace violence" (January 2020).1 Workplace violence experienced by doctors has been documented both in developed and developing countries. Reports show that Chinese medical professionals are being injured, disabled, or even killed by patients or their family members.2,3 The killing of a doctor in Beijing in 2019 attracted national attention. China's top legislative body has approved the first fundamental and comprehensive law to protect health-care professionals. The bill will take effect on June 1, 2020. However, the law alone will not solve the problem. We need to treat it from the root and address the issues that have led to this health-care-related violence. A multi-faceted action must be launched to reduce violence against doctors in China urgently.